Jagathy Raj, V P; Hari, V S; Gopikakumari, R(IEEE, 2012)
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Abstract:
The paper summarizes the design and implementation
of a quadratic edge detection filter, based on Volterra series,
for enhancing calcifications in mammograms. The proposed
filter can account for much of the polynomial nonlinearities
inherent in the input mammogram image and can replace the
conventional edge detectors like Laplacian, gaussian etc. The
filter gives rise to improved visualization and early detection of
microcalcifications, which if left undetected, can lead to breast
cancer. The performance of the filter is analyzed and found
superior to conventional spatial edge detectors
Description:
Data Science & Engineering (ICDSE), 2012 International Conference on
Tessamma, Thomas; Deepa, Sankar(Society for Imaging Informatics in Medicine, October , 2010)
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Abstract:
In this paper, a novel fast method for modeling mammograms
by deterministic fractal coding approach to detect
the presence of microcalcifications, which are early
signs of breast cancer, is presented. The modeled
mammogram obtained using fractal encoding method is
visually similar to the original image containing microcalcifications,
and therefore, when it is taken out from
the original mammogram, the presence of microcalcifications
can be enhanced. The limitation of fractal image
modeling is the tremendous time required for encoding.
In the present work, instead of searching for a matching
domain in the entire domain pool of the image, three
methods based on mean and variance, dynamic range of
the image blocks, and mass center features are used.
This reduced the encoding time by a factor of 3, 89, and
13, respectively, in the three methods with respect to
the conventional fractal image coding method with quad
tree partitioning. The mammograms obtained from The
Mammographic Image Analysis Society database
(ground truth available) gave a total detection score of
87.6%, 87.6%, 90.5%, and 87.6%, for the conventional
and the proposed three methods, respectively.
Description:
Journal of Digital Imaging, Vol 23, No 5 (October), 2010: pp 538-546